#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created by PyCharm.

@Date    : Tue May 12 2020 
@Time    : 22:55:56
@File    : classification.py
@Author  : alpha
"""

# from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim import lr_scheduler

from src.resnet import ResNet18
from src.repvgg import RepVGG18

models = {
    'resnet18': ResNet18,
    'repvgg18': RepVGG18
}


def train(args, model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.cross_entropy(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))


def test(args, model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.cross_entropy(output, target, reduction='sum').item()  # sum up batch loss
            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))
    return test_loss


def main():
    # Training settings
    parser = argparse.ArgumentParser(description='PyTorch Classification Example')
    parser.add_argument('--model', type=str, default='repvgg18',
                        help='model name')
    parser.add_argument('--batch-size', type=int, default=256, metavar='N',
                        help='input batch size for training (default: 128)')
    parser.add_argument('--test-batch-size', type=int, default=1024, metavar='N',
                        help='input batch size for testing (default: 512)')
    parser.add_argument('--epochs', type=int, default=200, metavar='N',
                        help='number of epochs to train (default: 200)')
    parser.add_argument('--lr', type=float, default=0.1, metavar='LR',
                        help='learning rate (default: 1)')
    parser.add_argument('--gamma', type=float, default=0.2, metavar='M',
                        help='Learning rate step gamma (default: 0.2)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--log-interval', type=int, default=100, metavar='N',
                        help='how many batches to wait before logging training status')

    parser.add_argument('--save-model', action='store_true', default=False,
                        help='For Saving the current Model')
    args = parser.parse_args()
    use_cuda = not args.no_cuda and torch.cuda.is_available()

    torch.manual_seed(args.seed)

    device = torch.device("cuda" if use_cuda else "cpu")

    kwargs = {'num_workers': 8, 'pin_memory': True} if use_cuda else {}
    train_loader = torch.utils.data.DataLoader(
        datasets.cifar.CIFAR100('../data', train=True, download=True,
                                transform=transforms.Compose([
                                    transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1),
                                    transforms.RandomHorizontalFlip(),
                                    transforms.ToTensor(),
                                    transforms.Normalize((0.5, 0.5, 0.5), (1, 1, 1))
                                ])),
        batch_size=args.batch_size, shuffle=True, **kwargs)
    test_loader = torch.utils.data.DataLoader(
        datasets.cifar.CIFAR100('../data', train=False,
                                transform=transforms.Compose([
                                    transforms.ToTensor(),
                                    transforms.Normalize((0.5, 0.5, 0.5), (1, 1, 1))
                                ])),
        batch_size=args.test_batch_size, shuffle=True, **kwargs)


    model = models[args.model](num_classes=100).to(device)
    optimizer = optim.AdamW(model.parameters(), lr=args.lr)
    # optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9, weight_decay=1e-5)
    # scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, factor=args.gamma, min_lr=1e-6)
    scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[60, 120, 160, 200], gamma=args.gamma)
    for epoch in range(1, args.epochs + 1):
        train(args, model, device, train_loader, optimizer, epoch)
        test_loss = test(args, model, device, test_loader)
        scheduler.step()
        # scheduler.step(test_loss)

    if args.save_model:
        torch.save(model.state_dict(), "model.pt")


if __name__ == '__main__':
    main()